Docker Setup

I have setup the docker container to run the model evaluation.

Initial Query

I have a list of several thousand file paths in /tmp/files. Write a python script that merges the content int 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. "/tmp/files" is a list of file paths, one per line.

LLM Generation

Query

I have a list of several thousand file paths in /tmp/files. Write a python script that merges the content int 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. "/tmp/files" is a list of file paths, one per line.

Output

Here's a Python script that merges the content of the files listed in "/tmp/files" into 16 approximately equal-sized files named "/tmp/merged_0", "/tmp/merged_1", ..., "/tmp/merged_15": 

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import os
import math

# Read the file paths from "/tmp/files"
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Calculate the number of files per merged file
num_files = len(file_paths)
files_per_merge = math.ceil(num_files / 16)

# Open the merged files for writing
merged_files = [open(f"/tmp/merged_{i}", "w") for i in range(16)]

# Iterate over the file paths and merge their content into the corresponding merged file
for i, file_path in enumerate(file_paths):
    merged_file_index = i // files_per_merge
    with open(file_path, "r") as file:
        content = file.read()
        merged_files[merged_file_index].write(content)

# Close the merged files
for file in merged_files:
    file.close()
Here's how the script works: 1. We start by reading the file paths from "/tmp/files" using the `read()` method and splitting them into a list using `splitlines()`. This gives us a list of file paths stored in the `file_paths` variable. 2. We calculate the number of files per merged file by dividing the total number of files (`num_files`) by 16 and rounding up using `math.ceil()`. This ensures that each merged file will have approximately the same number of files. 3. We open 16 merged files for writing using a list comprehension. The merged files are named "/tmp/merged_0", "/tmp/merged_1", ..., "/tmp/merged_15". We store the file objects in the `merged_files` list. 4. We iterate over the `file_paths` list using `enumerate()` to get both the index (`i`) and the file path (`file_path`) for each file. 5. For each file, we calculate the index of the corresponding merged file by dividing the file index (`i`) by `files_per_merge`. This ensures that the files are distributed evenly among the merged files. 6. We open each file specified by `file_path`, read its content using `read()`, and write the content to the corresponding merged file using `write()`. 7. Finally, we close all the merged files using a loop. After running this script, you will have 16 merged files named "/tmp/merged_0", "/tmp/merged_1", ..., "/tmp/merged_15", each containing approximately the same amount of content from the original files listed in "/tmp/files". Note: Make sure that the user running the script has write permissions in the "/tmp" directory to create the merged files.

Extract Code

I extracted the following code from that output: 

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import os
import math

# Read the file paths from "/tmp/files"
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Calculate the number of files per merged file
num_files = len(file_paths)
files_per_merge = math.ceil(num_files / 16)

# Open the merged files for writing
merged_files = [open(f"/tmp/merged_{i}", "w") for i in range(16)]

# Iterate over the file paths and merge their content into the corresponding merged file
for i, file_path in enumerate(file_paths):
    merged_file_index = i // files_per_merge
    with open(file_path, "r") as file:
        content = file.read()
        merged_files[merged_file_index].write(content)

# Close the merged files
for file in merged_files:
    file.close()

Run Code Interpreter

Running the following program: 

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import os
import math

# Read the file paths from "/tmp/files"
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Calculate the number of files per merged file
num_files = len(file_paths)
files_per_merge = math.ceil(num_files / 16)

# Open the merged files for writing
merged_files = [open(f"/tmp/merged_{i}", "w") for i in range(16)]

# Iterate over the file paths and merge their content into the corresponding merged file
for i, file_path in enumerate(file_paths):
    merged_file_index = i // files_per_merge
    with open(file_path, "r") as file:
        content = file.read()
        merged_files[merged_file_index].write(content)

# Close the merged files
for file in merged_files:
    file.close()
And got the output:

PyFunc

final: False Resulting in output: False

Docker Setup

I have setup the docker container to run the model evaluation.

Initial Query

I have a list of several thousand file paths in /tmp/files. Write a python script that merges the content int 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. "/tmp/files" is a list of file paths, one per line.

LLM Generation

Query

I have a list of several thousand file paths in /tmp/files. Write a python script that merges the content int 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. "/tmp/files" is a list of file paths, one per line.

Output

Here's a Python script that merges the content of the files listed in "/tmp/files" into 16 files called "/tmp/merged_%d" (where %d is replaced by numbers from 0 to 15) with approximately the same size: 

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import os
import math

# Read the file paths from /tmp/files
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Calculate the number of files per merged file
num_files = len(file_paths)
files_per_merge = math.ceil(num_files / 16)

# Initialize a list to store the file handles for the merged files
merged_files = [open(f"/tmp/merged_{i}", "w") for i in range(16)]

# Merge the content of the files into the merged files
for i, file_path in enumerate(file_paths):
    merge_index = i // files_per_merge
    with open(file_path, "r") as file:
        content = file.read()
        merged_files[merge_index].write(content)

# Close all the merged file handles
for file in merged_files:
    file.close()
Here's how the script works: 1. We start by reading the file paths from "/tmp/files" using the `open()` function and splitting the content into a list of file paths using `splitlines()`. 2. We calculate the number of files per merged file by dividing the total number of files by 16 and rounding up using `math.ceil()`. This ensures that each merged file will contain approximately the same number of files. 3. We initialize a list called `merged_files` to store the file handles for the merged files. We open 16 files with names "/tmp/merged_0" to "/tmp/merged_15" in write mode using a list comprehension. 4. We iterate over the file paths using `enumerate()` to get both the index and the file path. - We calculate the merge index by dividing the current index by `files_per_merge`. This determines which merged file the current file should be merged into. - We open the current file, read its content, and write it to the corresponding merged file using the `write()` method. 5. Finally, we close all the file handles in the `merged_files` list to ensure proper file handling. After running this script, the content of the files listed in "/tmp/files" will be merged into 16 files called "/tmp/merged_0" to "/tmp/merged_15", with each merged file containing approximately the same number of files.

Extract Code

I extracted the following code from that output: 

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import os
import math

# Read the file paths from /tmp/files
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Calculate the number of files per merged file
num_files = len(file_paths)
files_per_merge = math.ceil(num_files / 16)

# Initialize a list to store the file handles for the merged files
merged_files = [open(f"/tmp/merged_{i}", "w") for i in range(16)]

# Merge the content of the files into the merged files
for i, file_path in enumerate(file_paths):
    merge_index = i // files_per_merge
    with open(file_path, "r") as file:
        content = file.read()
        merged_files[merge_index].write(content)

# Close all the merged file handles
for file in merged_files:
    file.close()

Run Code Interpreter

Running the following program: 

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import os
import math

# Read the file paths from /tmp/files
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Calculate the number of files per merged file
num_files = len(file_paths)
files_per_merge = math.ceil(num_files / 16)

# Initialize a list to store the file handles for the merged files
merged_files = [open(f"/tmp/merged_{i}", "w") for i in range(16)]

# Merge the content of the files into the merged files
for i, file_path in enumerate(file_paths):
    merge_index = i // files_per_merge
    with open(file_path, "r") as file:
        content = file.read()
        merged_files[merge_index].write(content)

# Close all the merged file handles
for file in merged_files:
    file.close()
And got the output:

PyFunc

final: False Resulting in output: False

Docker Setup

I have setup the docker container to run the model evaluation.

Initial Query

I have a list of several thousand file paths in /tmp/files. Write a python script that merges the content int 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. "/tmp/files" is a list of file paths, one per line.

LLM Generation

Query

I have a list of several thousand file paths in /tmp/files. Write a python script that merges the content int 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. "/tmp/files" is a list of file paths, one per line.

Output

Here's a Python script that merges the contents of the files listed in "/tmp/files" into 16 files called "/tmp/merged_%d" (where "%d" is replaced by the numbers 0 to 15), ensuring that each merged file is approximately the same size: 

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import os
import math

# Read the file paths from /tmp/files
with open("/tmp/files", "r") as file:
    file_paths = file.read().splitlines()

# Calculate the number of files per merged file
num_files = len(file_paths)
files_per_merge = math.ceil(num_files / 16)

# Initialize variables
current_merge_file = 0
current_file_count = 0

# Open the initial merged file
merged_file = open(f"/tmp/merged_{current_merge_file}", "w")

# Iterate over each file path
for file_path in file_paths:
    # Open the file and read its content
    with open(file_path, "r") as file:
        content = file.read()

    # Write the content to the current merged file
    merged_file.write(content)

    # Increment the file count
    current_file_count += 1

    # Check if the current merged file has reached its limit
    if current_file_count == files_per_merge:
        # Close the current merged file
        merged_file.close()

        # Increment the merged file number
        current_merge_file += 1

        # Reset the file count
        current_file_count = 0

        # Open the next merged file if there are more files to process
        if current_merge_file < 16 and current_merge_file * files_per_merge < num_files:
            merged_file = open(f"/tmp/merged_{current_merge_file}", "w")

# Close the last merged file if it's still open
if not merged_file.closed:
    merged_file.close()
Here's how the script works: 1. It reads the file paths from "/tmp/files" into a list called `file_paths`. 2. It calculates the number of files per merged file by dividing the total number of files by 16 and rounding up to the nearest integer using `math.ceil()`. This ensures that each merged file will have approximately the same number of files. 3. It initializes variables to keep track of the current merged file number (`current_merge_file`) and the current file count within each merged file (`current_file_count`). 4. It opens the initial merged file with the name "/tmp/merged_0". 5. It iterates over each file path in `file_paths`: - It opens the file, reads its content, and writes it to the current merged file. - It increments the `current_file_count`. - If the `current_file_count` reaches the `files_per_merge` limit: - It closes the current merged file. - It increments the `current_merge_file` number. - It resets the `current_file_count` to 0. - If there are more files to process and the `current_merge_file` is less than 16, it opens the next merged file. 6. After iterating over all file paths, it checks if the last merged file is still open and closes it if necessary. This script will merge the contents of the files listed in "/tmp/files" into 16 files called "/tmp/merged_0", "/tmp/merged_1", ..., "/tmp/merged_15", with each merged file containing approximately the same number of files.

Extract Code

I extracted the following code from that output: 

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import os
import math

# Read the file paths from /tmp/files
with open("/tmp/files", "r") as file:
    file_paths = file.read().splitlines()

# Calculate the number of files per merged file
num_files = len(file_paths)
files_per_merge = math.ceil(num_files / 16)

# Initialize variables
current_merge_file = 0
current_file_count = 0

# Open the initial merged file
merged_file = open(f"/tmp/merged_{current_merge_file}", "w")

# Iterate over each file path
for file_path in file_paths:
    # Open the file and read its content
    with open(file_path, "r") as file:
        content = file.read()

    # Write the content to the current merged file
    merged_file.write(content)

    # Increment the file count
    current_file_count += 1

    # Check if the current merged file has reached its limit
    if current_file_count == files_per_merge:
        # Close the current merged file
        merged_file.close()

        # Increment the merged file number
        current_merge_file += 1

        # Reset the file count
        current_file_count = 0

        # Open the next merged file if there are more files to process
        if current_merge_file < 16 and current_merge_file * files_per_merge < num_files:
            merged_file = open(f"/tmp/merged_{current_merge_file}", "w")

# Close the last merged file if it's still open
if not merged_file.closed:
    merged_file.close()

Run Code Interpreter

Running the following program: 

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import os
import math

# Read the file paths from /tmp/files
with open("/tmp/files", "r") as file:
    file_paths = file.read().splitlines()

# Calculate the number of files per merged file
num_files = len(file_paths)
files_per_merge = math.ceil(num_files / 16)

# Initialize variables
current_merge_file = 0
current_file_count = 0

# Open the initial merged file
merged_file = open(f"/tmp/merged_{current_merge_file}", "w")

# Iterate over each file path
for file_path in file_paths:
    # Open the file and read its content
    with open(file_path, "r") as file:
        content = file.read()

    # Write the content to the current merged file
    merged_file.write(content)

    # Increment the file count
    current_file_count += 1

    # Check if the current merged file has reached its limit
    if current_file_count == files_per_merge:
        # Close the current merged file
        merged_file.close()

        # Increment the merged file number
        current_merge_file += 1

        # Reset the file count
        current_file_count = 0

        # Open the next merged file if there are more files to process
        if current_merge_file < 16 and current_merge_file * files_per_merge < num_files:
            merged_file = open(f"/tmp/merged_{current_merge_file}", "w")

# Close the last merged file if it's still open
if not merged_file.closed:
    merged_file.close()
And got the output:

PyFunc

final: False Resulting in output: False

Docker Setup

I have setup the docker container to run the model evaluation.

Initial Query

I have a list of several thousand file paths in /tmp/files. Write a python script that merges the content int 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. "/tmp/files" is a list of file paths, one per line.

LLM Generation

Query

I have a list of several thousand file paths in /tmp/files. Write a python script that merges the content int 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. "/tmp/files" is a list of file paths, one per line.

Output

Here's a Python script that merges the contents of the files listed in "/tmp/files" into 16 files called "/tmp/merged_%d" (where %d is replaced by numbers from 0 to 15), ensuring that each merged file is approximately the same size: 

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import os

# Read the file paths from /tmp/files
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Calculate the total size of all files
total_size = sum(os.path.getsize(file_path) for file_path in file_paths)

# Calculate the approximate size of each merged file
merged_file_size = total_size // 16

# Initialize variables
current_size = 0
current_merged_file_index = 0
current_merged_file = None

# Iterate over the file paths
for file_path in file_paths:
    # Open the file and read its content
    with open(file_path, "r") as file:
        content = file.read()

    # If the current merged file doesn't exist or its size exceeds the limit,
    # close the current merged file and open a new one
    if current_merged_file is None or current_size + len(content) > merged_file_size:
        if current_merged_file is not None:
            current_merged_file.close()
        current_merged_file_index += 1
        current_merged_file = open(f"/tmp/merged_{current_merged_file_index}", "w")
        current_size = 0

    # Write the content to the current merged file
    current_merged_file.write(content)
    current_size += len(content)

# Close the last merged file
if current_merged_file is not None:
    current_merged_file.close()
Explanation: 1. We start by reading the file paths from "/tmp/files" using `file_list.read().splitlines()`, which gives us a list of file paths. 2. We calculate the total size of all files by summing the sizes of each file using `os.path.getsize()`. 3. We calculate the approximate size of each merged file by dividing the total size by 16 (since we want to create 16 merged files). 4. We initialize variables to keep track of the current merged file index, the current merged file object, and the current size of the merged file. 5. We iterate over the file paths and perform the following steps for each file: - Open the file and read its content. - If the current merged file doesn't exist or its size exceeds the approximate merged file size, we close the current merged file (if it exists) and open a new merged file with the next index. - Write the content of the file to the current merged file. - Update the current size of the merged file by adding the length of the content. 6. After iterating over all file paths, we close the last merged file (if it exists). This script will merge the contents of the files listed in "/tmp/files" into 16 files called "/tmp/merged_%d" (where %d is replaced by numbers from 0 to 15), ensuring that each merged file is approximately the same size.

Extract Code

I extracted the following code from that output: 

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import os

# Read the file paths from /tmp/files
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Calculate the total size of all files
total_size = sum(os.path.getsize(file_path) for file_path in file_paths)

# Calculate the approximate size of each merged file
merged_file_size = total_size // 16

# Initialize variables
current_size = 0
current_merged_file_index = 0
current_merged_file = None

# Iterate over the file paths
for file_path in file_paths:
    # Open the file and read its content
    with open(file_path, "r") as file:
        content = file.read()

    # If the current merged file doesn't exist or its size exceeds the limit,
    # close the current merged file and open a new one
    if current_merged_file is None or current_size + len(content) > merged_file_size:
        if current_merged_file is not None:
            current_merged_file.close()
        current_merged_file_index += 1
        current_merged_file = open(f"/tmp/merged_{current_merged_file_index}", "w")
        current_size = 0

    # Write the content to the current merged file
    current_merged_file.write(content)
    current_size += len(content)

# Close the last merged file
if current_merged_file is not None:
    current_merged_file.close()

Run Code Interpreter

Running the following program: 

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import os

# Read the file paths from /tmp/files
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Calculate the total size of all files
total_size = sum(os.path.getsize(file_path) for file_path in file_paths)

# Calculate the approximate size of each merged file
merged_file_size = total_size // 16

# Initialize variables
current_size = 0
current_merged_file_index = 0
current_merged_file = None

# Iterate over the file paths
for file_path in file_paths:
    # Open the file and read its content
    with open(file_path, "r") as file:
        content = file.read()

    # If the current merged file doesn't exist or its size exceeds the limit,
    # close the current merged file and open a new one
    if current_merged_file is None or current_size + len(content) > merged_file_size:
        if current_merged_file is not None:
            current_merged_file.close()
        current_merged_file_index += 1
        current_merged_file = open(f"/tmp/merged_{current_merged_file_index}", "w")
        current_size = 0

    # Write the content to the current merged file
    current_merged_file.write(content)
    current_size += len(content)

# Close the last merged file
if current_merged_file is not None:
    current_merged_file.close()
And got the output:

PyFunc

Traceback (most recent call last): File "/usr/src/app/check.py", line 20, in <module> print('final: ' + str(test())) ^^^^^^ File "/usr/src/app/check.py", line 6, in test sz.append(len(open("/tmp/merged_%d" % i).read())) ^^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: '/tmp/merged_0' Resulting in output: False

Docker Setup

I have setup the docker container to run the model evaluation.

Initial Query

I have a list of several thousand file paths in /tmp/files. Write a python script that merges the content int 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. "/tmp/files" is a list of file paths, one per line.

LLM Generation

Query

I have a list of several thousand file paths in /tmp/files. Write a python script that merges the content int 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. "/tmp/files" is a list of file paths, one per line.

Output

Here's a Python script that merges the content of several thousand file paths listed in "/tmp/files" into 16 approximately equal-sized files: 

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import os
import math

# Read the file paths from "/tmp/files"
with open("/tmp/files", "r") as f:
    file_paths = f.read().splitlines()

# Calculate the approximate number of files per merged file
num_files = len(file_paths)
files_per_merged = math.ceil(num_files / 16)

# Create and open the merged files
merged_files = [open(f"/tmp/merged_{i}", "w") for i in range(16)]

# Merge the content of the files into the corresponding merged files
for i, file_path in enumerate(file_paths):
    merged_file_index = i // files_per_merged
    with open(file_path, "r") as f:
        content = f.read()
        merged_files[merged_file_index].write(content)

# Close the merged files
for merged_file in merged_files:
    merged_file.close()
Here's how the script works: 1. We start by reading the file paths from "/tmp/files" using `open()` and `read()`. We split the content into lines using `splitlines()` to get a list of file paths. 2. We calculate the approximate number of files that should be merged into each of the 16 merged files. We use `math.ceil()` to round up the result to ensure that all files are included. 3. We create and open the 16 merged files using a list comprehension. The files are named "/tmp/merged_0", "/tmp/merged_1", and so on, up to "/tmp/merged_15". 4. We iterate over each file path in the `file_paths` list using `enumerate()` to get both the index and the file path. 5. For each file path, we calculate the index of the corresponding merged file by dividing the file index by `files_per_merged`. This ensures that the files are distributed evenly among the merged files. 6. We open the file at the current file path, read its content using `read()`, and write the content to the corresponding merged file using `write()`. 7. After processing all the files, we close the merged files using a loop. After running this script, you will have 16 files named "/tmp/merged_0", "/tmp/merged_1", and so on, up to "/tmp/merged_15". Each merged file will contain the content of approximately the same number of files from the original list of file paths. Note: Make sure that the script has write permissions to create and write to the "/tmp/merged_*" files.

Extract Code

I extracted the following code from that output: 

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import os
import math

# Read the file paths from "/tmp/files"
with open("/tmp/files", "r") as f:
    file_paths = f.read().splitlines()

# Calculate the approximate number of files per merged file
num_files = len(file_paths)
files_per_merged = math.ceil(num_files / 16)

# Create and open the merged files
merged_files = [open(f"/tmp/merged_{i}", "w") for i in range(16)]

# Merge the content of the files into the corresponding merged files
for i, file_path in enumerate(file_paths):
    merged_file_index = i // files_per_merged
    with open(file_path, "r") as f:
        content = f.read()
        merged_files[merged_file_index].write(content)

# Close the merged files
for merged_file in merged_files:
    merged_file.close()

Run Code Interpreter

Running the following program: 

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import os
import math

# Read the file paths from "/tmp/files"
with open("/tmp/files", "r") as f:
    file_paths = f.read().splitlines()

# Calculate the approximate number of files per merged file
num_files = len(file_paths)
files_per_merged = math.ceil(num_files / 16)

# Create and open the merged files
merged_files = [open(f"/tmp/merged_{i}", "w") for i in range(16)]

# Merge the content of the files into the corresponding merged files
for i, file_path in enumerate(file_paths):
    merged_file_index = i // files_per_merged
    with open(file_path, "r") as f:
        content = f.read()
        merged_files[merged_file_index].write(content)

# Close the merged files
for merged_file in merged_files:
    merged_file.close()
And got the output:

PyFunc

final: False Resulting in output: False